Computational Biology and Machine Learning

In theory, there is no difference between theory and practice. But, in practice, there is.

-- Jan L.A. van de Snepscheut

Welcome to the Computational Biology group!

Our research group develops new methods for the statistical analysis of data. The major focus of our research is guided by high-dimensional data from genomics experiments.

Currently, we are especially interested in the analysis of gene expression data from microarray experiments on a pathways level. In close collaboration with groups from Biology and Medicine we are studying cancer related questions. An ultimate goal of our research is to contribute to the deciphering of regulatory networks with respect to their reconstruction and functional analysis to shed light on causal mechanisms underlying complex diseases.

Our methodological research aims to develop and improve computational and statistical methods in exploratory data analysis, graph theory, machine learning, Monte Carlo methods, multivariate analysis, optimization and statistical inference to apply them to problems in Computational Biology and Medical Bioinformatics.

Research

• Computational Biology
• Biostatistics
• Statistical Machine Learning
• Network Biology

For a list of current projects click here.

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Selected Publications

1. G. Altay and F. Emmert-Streib
   Revealing differences in gene network inference algorithms on the network level by ensemble methods
   Bioinformatics 26(14):1738 - 1744 (2010).
   
2. F. Emmert-Streib and M. Dehmer
   Medical Biostatistics for Complex Diseases
   Wiley-Blackwell (2010).
   
3. G. Glazko and F. Emmert-Streib
   Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets
   Bioinformatics 25(18):2348-2354 (2009).
   
4. F. Emmert-Streib and M. Dehmer
   Information processing in the transcriptional regulatory network of yeast: Functional robustness
   BMC Systems Biology 3:35 (2009).
   
5. F. Emmert-Streib and M. Dehmer
   Fault tolerance of information processing in gene networks
   Physica A: Statistical Mechanics and its Applications 388(4) (2009) 541-548.
   
6. F. Emmert-Streib and M. Dehmer
   Information Theory and Statistical Learning
   Springer (2008).
   
7. F. Emmert-Streib and M. Dehmer
   Analysis of Microarray Data: A Network-Based Approach
   Wiley-VCH (2008).
   
8. M. Dehmer and F. Emmert-Streib
   Structural information content of networks: Graph entropy based on local vertex functionals
   Computational Biology and Chemistry, 32(2) (2008) 131-138.
   
9. F. Emmert-Streib
   The Chronic Fatigue Syndrome: A Comparative Pathway Analysis
   Journal of Computational Biology, 14(7) (2007) 961-972.
   
10. L. Chen, F. Emmert-Streib and J. Storey
    Harnessing naturally randomized transcription to infer regulatory relationships among genes
    Genome Biology, 8:R219 (2007).
    
11. F. Emmert-Streib and A. Mushegian
    A Topological Algorithm for Identification of Structural Domains of Proteins
    BMC Bioinformatics, (2007) 8-237.
    
12. F. Emmert-Streib and M. Dehmer
    Information Theoretic Measures of UHG Graphs with Low Computational Complexity
    Applied Mathematics and Computation, 190(2) (2007) 1783-1794.
    
13. F. Emmert-Streib
    Algorithmic Computation of Knot Polynomials of Secondary Structure Elements of Proteins
    Journal of Computational Biology, 13(8) (2006) 1503-1512.
    
14. F. Emmert-Streib
    A Heterosynaptic Learning Rule for Neural Networks
    International Journal of Modern Physics C 17(10) (2006) 1501-1520.
    

More publications can be found here.

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Contact

Dr. Frank Emmert-Streib
 

Institute:	General Contact:
Queen's University Belfast  Computational Biology and Machine Learning  Center for Cancer Research and Cell Biology  School of Medicine, Dentistry and Biomedical Sciences	Phone: +44 (0)28 9097 2792 Fax: +001-816-926-4668 E-Mail (click here)
Postal Address:
Center for Cancer Research and Cell Biology  Queen's University Belfast  97 Lisburn Road, Belfast BT9 7BL, UK

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